Esterified Hyaluronic Acid Placed in the Middle Ear Does Not Improve Outcomes in Cholesteatoma Surgery
Bibliographic record
Abstract
BACKGROUND: The aim of this article is to assess the efficacy of esterified hyaluronic acid as a barrier to formation of adhesions and improvement of tympanomastoid ventilation. METHODS: A prospective cohort analysis was performed at a tertiary referral centre. 126 ears were analysed in children with cholesteatoma. Esterified hyaluronic acid was placed on the promontory of 63 ears at primary canal wall intact surgery for cholesteatoma. No esterified hyaluronic acid was used in 63 control ears. Cholesteatoma recurrence, histopathological analysis of scar tissue following second-stage procedure, and middle ear pressure were the main outcome measures. RESULTS: At 5 years, esterified hyaluronic acid (7%) and non-esterified hyaluronic acid (10%) did not differ in cholesteatoma recurrence (Kaplan- Meier log rank analysis, P=.52). Esterified hyaluronic acid (n=11) and non-esterified hyaluronic acid (n=2) ears formed scar at the site of packing material (n=11) (Fisher's exact test, P=.04). Foamy histiocytes/macrophages were found in esterified hyaluronic acid (n=15) and non-esterified hyaluronic acid ears (n=1) (Fisher's exact test, P-125 daPa) in 44% (14/32) esterified hyaluronic acid ears and 42% (15/36) non-esterified hyaluronic acid ears (P=1.0, Fisher's exact test). CONCLUSIONS: We have discontinued the use of esterified hyaluronic acid in cholesteatoma surgery due to lack of detectable benefit. Esterified hyaluronic acid in the middle ear neither reduces cholesteatoma recurrence nor appears to improve the ventilation of the middle ear. Furthermore, esterified hyaluronic acid alters the inflammatory process within the middle ear, the significance of which remains unclear.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".